In this paper, we propose a novel solution combining an event driven routing protocol, dynamic delivering scheme, and energy aware to support QoS requirements for three event types in multiple event WSN. Simulation results show that, the proposed solution significantly reduces packet loss rate for high reliability requirement events and extends the network lifetime of multievent WSN.
Kỹ thuật điều khiển & Điện tử NOVEL ENERGY AWARE ROUTING PROTOCOL FOR MULTIEVENT WIRELESS SENSOR NETWORK Nguyen Thi Thu Hang*, Nguyen Chien Trinh, Nguyen Tien Ban Abstract: Multievent wireless sensor networks (WSN) such as smart buildings, intelligent environmental monitoring systems require different QoS (Quality of Service) provision based on various event types These networks contain large numbers of sensor nodes but they have a very limited power and processing capability, so efficient consumption is one of vital requirements for most WSNs Most of research papers in this area have dealt with one or two of QoS requirements or with a limited number of event types and event sources For this reason, in this paper, we propose a novel solution combining an event driven routing protocol, dynamic delivering scheme, and energy aware to support QoS requirements for three event types in multiple event WSN Simulation results show that, the proposed solution significantly reduces packet loss rate for high reliability requirement events and extends the network lifetime of multievent WSN Moreover, in case of high traffic load condition, sharing load over multiple paths would decrease latency for the urgent events in the multiple events network Keywords: Energy aware routing, Dynamic routing, Delivering scheme, Multievent, Wireless sensor network INTRODUCTION In some wireless sensor networks (WSN), there are different types of events based on their important levels Important events can be considered as abnormal situations Poisonous gas or liquid detection in chemical industry, fires in forest fire alarm systems are such kinds of events [1, 2] If the leakage occurs or wildfire happens, the monitor system must know it immediately Sometimes there may be several leaking points or wildfires, so there are multiple events appear in the network Then, it is more urgent to locate all of them It needs not only to locate the leaking points or hot spots but also to tell the volumes of leak or the burn areas Other environmental parameters, such as temperature, pressure, humidity, light intensity, and so on, can also be monitored and considered as normal events With WSNs for smart buildings, intelligent environmental monitoring, and industrial process [1-7], multiple events with different levels of importance may happen in the networks Take an example of forest fire alarm system, forest fire risk usually occurs during and after winters with little rain, after long periods of dry weather and during summer heat waves, and especially if such conditions coincide with strong winds The forest fire risk indicates the probability of a forest fire occurring For the forest fire alarm system, there are five danger levels of forest fire: level (very high): fires can start at any time, the sensor data must be transmitted quickly to the base station; level (high) and level (considerable): the sensor data should reach the sink with high reliability because it could indicate a possibility of forest fire, level (moderate) and level (low or none): the data is not too serious, so it can be transmitted without specific requirement of low latency or high reliability [8] Fire spots can appear in many different areas making various events with different levels of QoS requirements such as latency and reliability For most WSNs, energy efficient consumption is one of crucial requirements because sensors have limited power and processing capability [3] The wireless sensor node can only be equipped with a limited power source and in some application scenarios, replacement of power resources might be impossible Sensor node lifetime, therefore, 52 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ shows a strong dependence on battery lifetime So, many researchers have been focusing on the design of power-aware protocols and algorithms for sensor networks [9-11] To meet these requirements of QoS and energy efficiency, there are three essential approaches as follows First, for providing different levels of reliability requirements, there have been many techniques that many researchers are interested in, in which routing is one of the most important techniques There have been many research papers on single path routing and multipath routing protocols [12-17] Although the work of finding a single path is simple with low computational complexity and minimum resource utilization [12], [13], it could react slowly with the rapid change in the network topology (node or link failure) and can not support reliability as required by limited capacity of a single path [c11] So, many multipath routing protocols have been researched and developed to overcome the disadvantages of the single path routing protocols [15-17] In the case of many event types appear in the network which have different requirement of reliability, the dynamic routing scheme which combines single path for normal event type and multipath for high reliability requirement event type can be applied [14, 18] Second, splitting traffic over multiple paths could support the bandwidth requirements of different applications and reduce the probability of network congestion, then reduce network latency [19] Third, a lot of energy-efficient routing protocols have been proposed, they have been categorized and described in [10], [20-24], all of the protocols aimed at energy efficient consumption and expanding the network life time Besides, the technique of transmitting multiple copies of data packets over multiple paths in [14] will increases delivery reliability but the energy consumption would be much more times, that is a trade-off between energy and reliability So, applying energy-aware with event driven routing would even be more necessary to increase the energy efficiency in such multiple event WSNs To the best of our knowledge, all of research papers in this area have dealt with one or more requirements, and dealt with limited events and types of events There has been one research that raised the issue of challenges between a single-event wireless sensor network and multi-event wireless sensor network [23], but in the probable situation of concurrent events in the network, the research showed that it was unable to benefit effectively for data transmission over multiple paths than over single path, it provided shorter life time This is the first work that uses energy aware dynamic routing and packet delivering schemes to support the multi QoS requirements for multiple event type WSN In this paper, we proposes a combined solution for QoS provision, named EARPM (Energy Aware Routing Protocol for Multievent Wireless Sensor Network) for multievent wireless sensor network: to choose dynamic routing protocol and packet delivering scheme in WSN based on residual energy of nodes and different event types Our contributions in this paper are as follows: We propose a combination of dynamic routing scheme of single and multipath, and different packet delivering schemes of copying or splitting packets based on three event types to support the different reliability and latency requirements in multievent WSN We also propose an energy-aware algorithm to dynamically discover energy efficient paths for delivering event packets Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 53 Kỹ thuật điều khiển & Điện tử We implement our proposed routing and delivering schemes in OMNeT++ simulation to evaluate the adaptation of the network to the multiple event requirements and the efficiency of the energy aware scheme The paper is organized as follows: Section discusses the related work Section describes our proposed solution Section introduces our theory analyses The evaluation of our protocol based on computer simulation is presented in Section Finally the last section is the summarization and our future research work RELATED WORK Recently, there have been several research papers on multipath routing protocols and energy aware routing protocols to achieve various performance benefits In ReInForM (Reliable Information Forwarding Using Multiple Paths [14]), the source sends multiple copies of the same data through multiple paths to the sink Each packet is assigned a priority level based on the content of the information it contains The source computes the number of paths (or equivalently, the number of copies of the packet to be sent) based on the importance of the information, local channel error and distance from the sink ReInForM does not distinguish between the actual source and an intermediate forwarding node Next hops are usually chosen among the nearest hops to the sink, otherwise they would be chosen randomly This helps in load balancing and avoids the nodes on the “better” path to be quickly energy depletion However, sending multiple copies of all packets would waste energy and the routing protocol has not considered the latency of the event The research has considered only single event source scenario, not multiple events A low-interference energy-efficient multipath routing protocol (LIEMRO) has been designed for improving QoS in event-based WSN [23] This protocol has discovered multiple interference-minimized node disjoint paths between source node and sink node and included a load balancing algorithm to distribute source node's traffic over multiple paths based on the relative quality of each path The simulation shows that in high traffic load conditions, it can increase data reception rate, lengthen the network life time, and significantly reduce end-to-end latency compared with single path routing approach The research has raised the issue of challenges between a single-event wireless sensor network and multi-event wireless sensor network LIEMRO tries to construct node-disjoint paths for each detected event Nevertheless, paths with shared nodes are probable when two or more events occur in the network Therefore, the research has also evaluated LIEMRO in multiple event situations The simulated results show that in this situation, LIEMRO is unable to benefit effectively for data transmission over multiple paths than over single path, it provides shorter life time In [18], a multipath routing protocol has been proposed in which the sink discovers paths based on path weight factor by using link efficiency, energy ratio, and hop distance The sink selects the number of paths among the available paths based upon the criticalness of an event, and if the event is non-critical, then single path with highest path weight factor is selected, otherwise multiple paths are selected for the reliable communication So this research has just differentiated two types of events In [25] a distributed, scalable and localized multipath search protocol has been introduced to discover multiple node-disjoint paths between the sink and source nodes In this research, a load balancing algorithm is used to distribute the traffic over the multiple paths discovered, it allows the sink node to allocate traffic based on paths' cost, which depends on the energy levels and the hop distances of nodes along each paths The 54 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ proposed scheme has been compared to the directed diffusion [26], directed transmission, and the energy aware routing [9] protocols Simulation results show that it has higher node energy efficiency, low average delay But the research uses limited number of sinksource scenario, one sink with two or four sources, two sinks with three sources, and has not considered different packet types From the above analyses, it can be seen that all of these research works have just dealt with only one or two event types which require QoS requirements of latency and/or reliability, some work has considered the energy efficiency but has not investigated the scenario of concurrent events, there has not been any research supported diversity QoS requirements for multievent WSN Our proposal in this paper is to discovering energy-aware single and multiple paths, and use dynamic load delivering scheme which adapt to the three types of events, consequently it supports better performance for different event requirements of reliability, latency and energy efficiency for multievent WSN PROPOSED SOLUTION Based on the variety QoS requirements of multievent WSN and the benefits in getting high reliability and low latency of multipath routing protocols, we propose our novel energy aware dynamic routing protocol for multievent WSN Our routing protocol is a renovation work from GPSR single path routing protocol [27] for event trigger routing WSN, so only greedy forwarding technique is applied when event appears in the network There are three dynamic changes have been done for the scheme First, based on the type of events, source node chooses single path for normal event type (named A, which does not require high reliability and low latency), multiple paths for the higher requirement event types (named B, which requires higher reliability, and C, which requires lower latency because of its urgency) Second, the delivering schemes are different from B and C: for B, data packets from source nodes should be copied and forwarded over two paths simultaneously while for C, packets should be split and sent over two paths Third, to avoid quickly depleted energy node on the shortest path, nodes in the network would choose the relay node(s) having residual energy more or equal to the average residual energy of all live and sink-nearer neighbors We consider the average value of energy because time after time, the relay node will turn over among alive neighbors due to their residual energy levels have decreased by the time packets of an event passed by, so nodes will deplete their energy more slowly and equally Choosing the average value is better than choosing the highest residual energy value because the highest residual energy neighbor node might have the longer distance to the sink, so the energy consumption would be higher Furthermore, that highest residual energy neighbor node could be the good neighbor of other event node both in energy and distance to the sink in the multiple event network, so it should be chosen as the relay node of the other node Fig.1 shows a description of our dynamic routing schemes in multievent WSN Source nodes have to find the best neighbor(s) among the sink-nearer ones to deliver its sensed data packets and relay nodes have to find only one best neighbor There are five alive neighboring nodes (1, 2, 3, 5, 9) and one dead node (12) of the source in which only four nodes are alive sink-nearer (1, 2, 3, 9) For single path GPSR routing: there is one that is alive and nearest to the sink (node 3) So, source node would choose node to be the best relay node on the routing path to the sink (Fig 1.a) Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 55 Kỹ thuật điều khiển & Điện tử For multipath routing: the four alive nodes can be chosen in priority order of 3, 2, 1, and if only shortest distance evaluation is used (Fig 1.b) For EARPM: we consider three criteria in order of priority: (1) neighbor’s residual energy, (2) distance from neighbor node to the sink, and (3) distance from source node to neighbor node Then, at time, the residual energy of node is the highest and node is the second highest, the residual energy of node is equal to node 1, the distances from neighbor nodes to sink are in order 3, 2, 9, as closer to the sink, and the distances from source node to its neighbors are in order of 1, 9, 2, as nearer to the source Then, the priority order of paths is 2, 9, 3, and Source A would choose as the delay node while source B and C would choose and as the delay nodes (Fig 1.c) A Network Model The WSN can be viewed as an undirected graph G V , E where V represents the set d Source-BS 12 dmax 11 10 Source A a) Single path GPSR routing d Source-BS dmax 12 11 10 Source B/C SINK 13 b) Multipath routing of vertices (sensor nodes and sink) and E represents the set of edges We assume there are N S sensor nodes randomly place in an area ( S S m ) , there exists a link E i, j between d Source-BS node i and node j if the Euclidean distance Euclidean i, j is not larger than the sensor node’s radio transmission radius d max There is a single monitoring node (sink) at the center of sensing area, it knows its position and all nodes’ position When sensor node detects an event, it will send its data directly to the sink if its distance to sink is less or equal to its transmission range or indirectly over its neighbors otherwise B Energy Model In our work, a simple radio model where the radio dissipates Eelec energy per bit to run the transmitter or receiver and amp energy SINK 13 B,C dmax 12 Source A/B/C 11 10 A,B,C SINK 13 E >E >Eaverage >E3 =E remained energy c) EARPM routing Figure A description of the combining of energy aware single and multiple path routing schemes per bit for the transmit amplifier We also assume d energy loss due to channel 56 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ transmission [28] So, the energy consumption to send a L -bit packet to next hop at a distance of d is: (1) Ehop Eelec L amp L d L bit packet Transmit Electronics d amp*L*d Eelec*L L bit packet Tx Amplifier Receive Electronics Eelec*L Figure Energy model – first order radio model C Proposed Routing Scheme Fig shows a brief description of our EARPM operation when node detects an event or receives routing request from its neighbor node, then it has to select relay node(s) for delivering sensed data packets afterward When sensor node detects an event, it will send routing requests to all of its alive neighbors, then all alive neighbors will send their routing requests toward all of their alive neighbors and so on After that, the source and all other related nodes will receive reply packets with the information of their neighbors’ residual energy to determine the node(s) which is/are eligible to be selected as next hop relay If a node’s residual energy Eresidual is less than Edead then it can not send reply REQ message, if the residual energy is less than Ethreshold then node can not send or forward data packets Only source node has to decide the number of paths for its sensed data based on the event type while all forwarding nodes have to choose only one best relay node If the distance to sink is equal or less than d max (the maximum transmission range of sensor), then node directly sends data to the sink If not, sensor node will have to find the best neighbors to deliver its data to the sink One or two best neighbors will be chosen based on three criteria: first, its/their residual energy (the neighbor’s residual energy is equal or larger than the average energy of all alive sink-nearer neighbors eResidualNeighbor[i]>= tempEAvg); second, among the neighbors that satisfy the first criteria, one or two neighbors that have shortest distance to the sink (as closer to the sink as possible) would be chosen; third, if there are neighbors that satisfy the previous two criteria, the order of best neighbors would depend on the distance from a neighbor to the source node (as closer as possible) In this section, we analyze packet latency and make the probabilistic formulation of reliability for both single-path and multipath routing The results show that load-sharing on multipath would reduce the queuing time of packets in congested situation, then reduce the packet latency in a simple way, and multi-path routing with redundant transmission is effective in increasing the reliability Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 57 Kỹ thuật điều khiển & Điện tử BEGIN N Node detect event Node receive REQ message (from other node) Y Eremain ≥ E dead Node has enough energy to reply back or not? Y Send back a REP message with information of its residual energy Node has enough energy to deliver data packet or not? Eremain ≥ E threshold Y d2SINK≤dmax Y Do not have to build routing table, sink can be reached directly N Send REQ messages to all of its alive neighbors Receive REP message(s) from the neighbor(s) Calculate the average residual energy of its alive neighbor(s) Maximum two best neighbors would be selected as relaying node(s) based on alive neighbors’ residual energy and distances for (i=0;i=eThreshold) { tempE=eResidualNeighbor[i]; tempETotal= tempETotal+tempE; numOfneighborLive++; } } tempEAvg= tempETotal/numOfneighborLive; END Figure Description of relay selection operation THEORETICAL ANALYSES A Latency analysis The total delay, denoted as d , experienced by a packet in a path of hop count h is the sum of the delays at the intermediate nodes, d j (where j 1, 2, , h ), and is given by h d dj (2) j 1 Considering the propagation and processing delays as negligible, d j can be calculated as follows (3) d j dtrans d MAC d que 58 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ where dtrans is the transmission delay, d MAC is the medium access delay and d que is the queuing delay of a packet In this paper, we concentrate into the queuing delay of a packet Queuing delay at any node depends on the queue service time, number of packets in queue, and the packet arrival pattern Fig shows the analysis of the queuing delay of packets We compare the queuing delay of packets over single and multiple paths using redundant transmission and loadsharing schemes From source nodes, there are three event type packets that would enter queues with the current queue length of Q* packets over a maximum capacity of Q packets As we can see from Fig 4, for event type A and B packets, only N packets would be sent over one path, so the average queuing delay of packet type A and B is equal and can be approximately calculated as the delay of the middle packet N / For type C, it is less and proportional to the inversion of M - the number of paths, which can be calculated as d queA d queB (Q * d queC (Q * N ) d service N ) d service 2 M (4) (5) Figure Occupation of queue for the three event types If we denote the improvement of latency of C over A is limprovement , we can evaluate the improvement at one queue as follows limprovement d queB d queC d queB N NM 100% 100% 2Q *N (6) Let take Q* x N , then Eq.6 can be shortened as Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 59 Kỹ thuật điều khiển & Điện tử limprovement 1 M 100% 2 x 1 (7) From Eq 7, it is clear that splitting data packets over multiple paths would decrease the load placed on each link, thus reducing the packet processing time, the larger the number of multipath (larger M), the better the advantage of latency of C over A, the larger the queue (larger x), the lesser the advantage of latency of C over A Based on the fact that sensor nodes have limited memory [3, 11], we can see that the sensor queue capacity can not be so large, so the value of limprovement can have a great value Fig is a specific example for the mathematical latency comparison of multipath routing using load sharing technique Figure Latency comparison of multipath routing over single path routing with using load sharing technique over single path routing different numbers of paths and with different numbers of paths and queue length queue length B Reliability analysis If the number of original packets sent by the source is N S , and the number of distinctive packets received by the sink is N R , the reliability, denoted as R , is R N r / N s Here the distinctive packet means that if sink receives multiplicative packets (the original data packet and the copy one), it considers those as one received packet Reliability of Single-Path Routing Consider a source and a sink which are h hops apart as in Fig Let the per hop SINK channel packet error rate (PER) at Source PER c c c c th e1 e2 ej eh j hop in the path across the entire c network be a variable e j (where (1-ec1) (1-ec2) (1-ecj) (1-ech) ecj , and it is proportional to the distance), then the per-hop reliability at j th hop is (1 ecj ) Reliability h hops Figure Single path scenario The reliability of a path is a multiplicative metric Thus, the probability that a packet is received by the sink over a single path of h hops apart, p h , is h p h 1 ecj (8) j 1 Then single path packet error rate in this situation is 60 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ h (9) PER single p (h) 1 ecj j 1 Thus, in a multi hop sensor network, where channel errors could be very high and a source could be far away from the sink, a naïve forwarding scheme will result in a high PER, so single path routing is not proficient of attaining good reliability Reliability of Multipath Routing ec1,2 ec1,j Consider multiple paths from a ec1,h1 ec1,1 source to as in Fig There are M SINK Source th c c i c c paths and the hop count of the e 2,j e 2,h2 e 2,1 e 2,2 path is hi , the multipath packet error rate in this situation is the probability that all copy packets would suffer error in all paths The reliability of sending a packet by copying it and send over multiple paths can be calculated as ecM,hM ecM,1 ecM,2 ecM,j Figure Multipath scenario hi M M M PER multipath PERi single 1 pi hi 1 1 eic, j i 1 i 1 i 1 j 1 (10) where pi hi is the probability of success for the i t h path defined in Eq and eic, j is the probability that a packet is dropped at the j th hop of the i th path Then, the probability that at least one copy of a packet is successfully received by the sink over M paths, p M , is Figure Packet error rate evaluation based on the number of hops, paths for per-hop channel error rate of 1% hi M p M PER multipath 1 1 eic, j i 1 j 1 (11) Packets may be lost due to channel error and queue overflow; in such cases, sending multiple packets on multiple paths will improve the reliability or reduce PER Fig is a specific example for the mathematical PER evaluation of single-path and multipath routing with a per hop channel error rates of 1% As we can see, the higher the number of paths the better the reliability, and the larger the number of hops, the lower the reliability or the higher the PER Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 61 Kỹ thuật điều khiển & Điện tử There are also special cases where multipath data transmission does not meet the desired QoS requirements: Node has only one better neighbor towards the sink or sink is within the sensor node's transmission range The source node sends a packet over one path or sends it directly to the sink without specifying the type of data, so sending copies or splitting packets is not effective If events appear randomly in the network, there would be a large number of events is in this situation (in our simulation cases, there is approximately 25% of events appearing around the sink and source nodes send data directly to the sink) There are few forwarding nodes near the sink, causing the paths to converge at the front of the sink, in the heavy traffic situation they would create congestion and make longer delay and PER become worse The latency improvement could not be good in two cases of traffic: the traffic is so light that the queues are empty most of the time, and the traffic is too heavy and pushes Q* to the maximum capacitor Q A packet’s end-to-end delay is additive and depends on the number of hops, queues as well as network traffic, so it is difficult to estimate accurately and predictably by calculation (NP-complete problem), then it is a reasonable way to deal it with a heuristic technique PERFORMANCE EVALUATION A Simulation Parameters Table shows main parameters used in our OMNeT++ simulation [29] There are types of events (A, B and C) occurring in the sensor network with equivalent ratio We use several traffic scenarios in our simulation Each round (of 0.5 seconds), there are 2, 4, 5, 10, 20, and 25 nodes at random positions sending their data packets at random time, so the total average traffics of network are 25,5; 51; 64; 128, 255 and 319kbit/s respectively The sink is in the center of the sensing field, and 100 sensor nodes are uniform randomly placed For the reason of simplicity and to differentiate only the three event types, we use only single and two-path routing Table Simulation Parameters for EARPM Parameter Value Network size 500m x 500m Number of sensor nodes 100 Number of events per round 2, 4, 5, 10, 20, 25 Time interval (for one round) 0.5 sec Number of packets/event (burstLength) 40 Sensor node’s radio transmission radius 120m (dmax) Routing Information packet size (from Sink at the beginning to each node) 256 bits Route Request, Route Response packet size 24, 32 bits Data and Data Acknowledgement packet 128, bits size Link bit rate 30.720 bit/s Processing time (for a packet at queue) millisecond Queue size (Data packet) 200 PER of one hop (%) (10-rand(0,1))×10-2×(d/120)2/10 62 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ Initial Energy of each node 15-rand(0,1)×10-2 J Eelect 50nJ/bit amp 100pJ/bit/m2 Energy Threshold 0.1J The following performance parameters are assessed in the simulation: Network Lifetime: The lifetime of the multievent wireless sensor network is defined as the period of time from network initialization to the time first node dies, namely the minimum lifetime among all nodes Number of Dead Nodes: It is the number of nodes which have residual energy less than the threshold energy Packet Error Rate: It is a ratio of loss packets to packets sent For event type B, the loss packets are the packets that could not reach the sink even over the first or the second path and the packets sent are the original ones, not including the copy packets Latency: It is the total time taken to transmit the data from the event node to sink node B Result analyses In this section, simulation results show that our routing protocol could extend the network life time and adapt to the QoS requirements of multiple event types Lifetime extension and number of dead nodes We compare EARPM against the GPSR single path routing scheme where only distances from relay node to the sink and to the sending data node are considered Fig shows that when using the residual energy criteria, the lifetimes in all scenarios have been extended (approximately over 70%) Such experimental results demonstrate that the energy efficiency of our dynamic protocol is stable and has little impact by the increase of the event density (in not too heavy traffic condition) Figure Lifetime comparison of EARPM and GPSR single path routing protocols for multievent WSN The result in Fig 10 shows that as larger the number of event nodes sending packets in a round, as shorter the network lifetime This is true because when more event packets are Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 63 Kỹ thuật điều khiển & Điện tử sent to the sink, the energy consumption increases and nodes die earlier In our proposed scheme, the first dead node appears more slowly than in the GPSR single path routing scheme, that is because our scheme considers the average residual energy of nodes so it let nodes in the network use energy more equally Figure 10 Number of dead nodes vs simulation time routing in multievent WSN Packet Error Rate Fig 11 shows the result in PER for events/round simulation (other scenarios have the same results) It can be seen that the PER of event B is significantly improved compared to the A and C events Furthermore, during the simulation time from the first node dead in the distant scheme (1380 seconds) to that of the EARPM scheme (2400 seconds), PERs of all three event types in EARPM (denoted as E-A, E-B, and E-C) are less than in GPSR single path routing scheme (denoted as G-A, GB, and G-C) However, the PER of all event type will Figure 11 Analyses of data packet error rate of the three become greater after data types in multievent WSN the first node has dead with both routing schemes This is because dead nodes can not send or deliver any event data packet because they lack of energy 64 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ Latency Fig 12 shows result in latency of for events/round simulation We just consider the duration from the first event appearance to the time of 750 rounds (when network is in congested situation and all nodes still alive, then there are chances for multiple paths to be chosen) It can be seen that event C's packets have the smallest average latency, A is in the middle, and B has the highest latency The latency of C significantly improved over that of A and B because the packets of event C could split on two paths so the number of C packets on one path is reduced to half compared to A and B’s packets, that make the C’s path less congested than the others But, the improvement is good and distinct only with low queue occupancy (at first 750 rounds), it will be decreased when the queue is almost entirely occupied The latency of B increases as the flow packets of an event B are twice that of events A and C Since B sends the copy of the entire packets to the second line, B will cause much congestion on both paths of data and at time B’s packets may be merged into one path before reaching the sink Figure 12 Analyses of packet latency of the three data According to the types in multievent WSN simulation results, the proposed EARPM scheme with the dynamic routing based on event types and residual energy consideration brings contemporary benefits: smaller PER for event B, lower latency for event C and the lifetime of network is extended CONCLUSION AND FUTURE WORK The proposed QoS improvement solution for multievent wireless sensor networks shows that in terms of energy limitation, it significantly improves the network life time compared to the event driven shortest distant routing by considering the residual energy This is because node(s) with greater residual energy will have more chances to be the relay node(s), then the energy consumption would be more balanced over the network and nodes in the network burn energy more equally By sending redundant data on multipath, the protocol would greatly reduce packet error rates for high-reliability required events Furthermore, in congestion situation, splitting packets on multiple paths could lower the packet latency for urgent events In the future, we will continue to improve the quality of communications for multievent sensor networks based on priority queues so that they can better prioritize events that require high priority on latency and reliability in all network traffic conditions Acknowledgement: This work is partly supported by Motorola Solutions Foundation under Motorola scholarship and research funding program for ICT education Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 65 Kỹ thuật điều khiển & Điện tử REFERENCES [1] Y Liu, X Zhu, C Ma, L Zhang, “Multiple event detection in wireless sensor networks using compressed sensing,” in Proc ICT, Ayia Napa, Cyprus, May 8-11, 2011, pp 27-32, DOI: 10.1109/CTS.2011.5898935 [2] S Abdullah, S Bertalan, S Masar, A Coskun, I Castle, " A wireless sensor network for early forest fire detection and monitoring as a decision factor in the context of a complex integrated emergency response system," in IEEE Workshop EESMS, Milan, Italy, Jul 24-25, 2017, DOI: 10.1109/ EESMS.2017.8052688 [3] I.F Akyildiz, W Su, Y Sankarasubramaniam, E Cayirci, “A survey on sensor networks,” IEEE Commun Mag., Vol 40, no 8, pp 102-114, 2002, DOI: 10.1109/MCOM.2002.1024422 [4] M Z Hasan, H Al-Rizzo, F Al-Turjman, “A Survey on Multipath Routing Protocols for QoS Assurances in Real Time Wireless Multimedia Sensor Networks,” IEEE Commun Surveys & Tutorials, Vol 19, no 3, pp 1424 – 1456, 2017, DOI:10.1109/COMST.2017.2661201 [5] M Li, C Hua, C Chen, X Guan, “Application-driven virtual network embedding for industrial wireless sensor networks,” IEEE ICC, Paris, France, May 21-25, 2017, pp 1-6 [6] L Miller, K Abas, K Obraczka, “SCmesh: Solar-Powered Wireless Smart Camera Mesh Network,” in Proc ICCCN, Las Vegas, NV, USA, 2015, pp 1-8, DOI:10.1109/ICCCN.2015.7288462 [7] Y R V Prasad, R Pachamuthu, “Neural Network Based Short Term Forecasting Engine to Optimize Energy and Big Data Storage Resources of Wireless Sensor Networks,” in Proc IEEE COMPSAC, Taichung, Taiwan, Jul 15, 2015, pp 511-516, DOI:10.1109/COMPSAC.2015.264 [8] Natural Hazards Portal, “Danger levels forest fire,” Switzerland [Online] Available: http://www.natural-hazards.ch/home/dealing-with-natural-hazards/forestfire/danger-levels-forest-fire.html [9] R C Shah, H M Rabaey, “Energy aware routing for low energy ad hoc sensor networks,” in Proc IEEE WCNC, Orlando, FL, USA, Mar 17-21, 2002, pp 350– 355, DOI: 10.1109/WCNC.2002.993520 [10] M M Alam, M A Razzaque, M Mamun-Or-Rashid, C S Hong, “Energy-aware QoS provisioning for wireless sensor networks: Analysis and protocol,” IEEE J Commun Networks, Vol.11, no 4, pp 390-405, 2009 [11] M E E D A E Kader, A A A Youssif, A Z Ghalwash, “Energy Aware and Adaptive Cross-Layer Scheme for Video Transmission Over Wireless Sensor Networks,” IEEE Sensors Journal , Vol 16, no 1, pp 7792 – 7802, 2016 DOI:10.1109/JSEN.2016.2601258 [12] D Son, B Krishnamachari, J Heidemann, “Experimental Study of Concurrent Transmision in Wireless Sensor Networks,” in Proc SenSys ’06, Boulder, CO, USA, 31 Oct 31–Nov 3, 2006, pp 237–250 [13] J Kang, Y Zhang, B Nath, “End-to-End Channel Capacity Measurement for Congestion Control in Sensor Networks,” in Proc SANPA ’04, Boston, MA, USA, Aug 22, 2004 [14] B Deb, S Bhatnagar, B Nath, “ReInForM: reliable information forwarding using multiple paths in sensor networks,” in Proc IEEE Int Conf LCN’03, Bonn/Konigswinter, Germany, Germany, 2003, pp 406 – 415 DOI: 10.1109/LCN.2003.1243166 66 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” Nghiên cứu khoa học công nghệ [15] M Radi, B Dezfouli, K A Bakar, M Lee, “Multipath routing in wireless sensor networks: a survey and research challenges,” Sensors ISSN 1424-8220, Vol 12, no 1, pp 650-685, 2012 [16] M Masdari, M Tanabi, “Multipath routing protocols in wireless sensor networks: a survey and analysis,” IJFGCN, Vol 6, no 6, pp 181-192, 2013 [17] A A Anasane, R A Satao, “A survey on various multipath routing protocols in wireless sensor networks,” in Proc ICCCV 2016, Mumbai, India, 2016, pp 610– 615, DOI: 10.1016/j.procs.2016.03.077 [18] A V Sutagundar, S S Manvi, “Location aware event driven multipath routing in Wireless Sensor Networks: Agent based approach,” Egyptian Informatics Journal, Vol 14, no 1, pp 55–65, 2013 [19] J R Gallardo, A Gonzalez, L Villasenor-Gonzalez, J Sanchez, “Multipath Routing Using Generalized Load Sharing for Wireless Sensor Networks,” in Proc 7th IASTED Int Conf WOC2007, Montreal, QC, Canada, May 30–Jun 1, 2007, pp 374-379 [20] N A Pantazis, S A Nikolidakis, D D Vergados, “Energy-efficient routing protocols in wireless sensor networks: a survey,” IEEE Commun Surveys & Tutorials, Vol 15, no 2, pp 551-591, 2013 [21] W Lou, “An efficient N-to-1 multipath routing protocol in wireless sensor networks,” in Proc IEEE Int Conf MASS, DC, USA, Nov 7-10, 2005, pp 665672 [22] S Ehsan, B Hamdaoui, “A Survey on Energy-Efficient Routing Techniques with QoS Assurances for Wireless Multimedia Sensor Networks,” IEEE Commun Surveys & Tutorials, Vol 14, no 2, Second Quarter, pp 265–278, 2012 DOI:10.1109/SURV.2011.020211.00058 [23] M Radi, B Dezfouli, S A Razak, K A Bakar, “LIEMRO: A Low-Interference Energy-Efficient Multipath Routing Protocol for Improving QoS in Event-Based Wireless Sensor Networks,” in Proc SENSORCOMM, Venice, Italy, Jul 18-25, 2010, pp 551-557 DOI:10.1109/SENSORCOMM.2010.89 [24] S V Kalore, P Rewagad, “A review on efficient routing techniques in wireless sensor networks,” in Proc ICACEA, Ghaziabad, India, Mar 19-20, 2015, pp 803– 807, DOI:10.1109/ ICACEA.2015.7164813 [25] Y M Lu, V W S Wong, “An Energy-Efficient Multipath Routing Protocol for Wireless Sensor Networks,” in Proc IEEE VTC-2006, Montreal, QC, Canada, 25-28 Sep 2006, pp 2454–2459, DOI: 10.1109/VTCF.2006.505 [26] C Intanagonwiwat, R Govindan, D Estrin, J Heidemann, F Silva, "Directed diffusion for wireless sensor networking," IEEE/ACM Trans Networking, Vol 11, no 1, pp 2-16, Feb 2003, DOI:10.1145/345910.345920 [27] B Karp, H Kung, “Greedy Perimeter Stateless Routing”, in Proc 6th Annual ACM/IEEE Int Conf on Mobile Computing and Networking (Mobicom 2000), Boston, Massachusetts, USA, Aug 6-11, 2000, pp 243-254, DOI: 10.1145/345910.345953 A Chandrakasan, H Balakrishnan, “Energy-efficient [28] W.R Heinzelman, communication protocol for wireless microsensor networks,” in Proc HICSS, Maui, HI, USA, Jan 7, 2000 DOI:10.1109/HICSS.2000.926982 [29] Omnet++ version 4.4 [Online] Available: http://www.omnetpp.org/ Tạp chí Nghiên cứu KH&CN quân sự, Số 55, 06 - 2018 67 Kỹ thuật điều khiển & Điện tử TÓM TẮT GIAO THỨC ĐỊNH TUYẾN NHẬN THỨC NĂNG LƯỢNG MỚI CHO MẠNG CẢM BIẾN KHÔNG DÂY ĐA SỰ KIỆN Mạng cảm biến không dây (WSN) đa kiện ứng dụng tòa nhà thơng minh hay hệ thống giám sát môi trường yêu cầu cung cấp chất lượng dịch vụ (QoS) khác dựa kiểu loại kiện khác Các mạng chứa số lượng lớn nút cảm biến chúng có lượng khả xử lý giới hạn, tiêu thụ hiệu lượng yêu cầu thiết yếu Hầu hết báo nghiên cứu lĩnh vực đáp ứng hai yêu cầu QoS đáp ứng với số kiểu kiện số nguồn kiện có hạn Trong báo này, đề xuất giải pháp kết hợp giao thức định tuyến nhận thức lượng, định hướng kiện với chế truyền tin linh hoạt để hỗ trợ yêu cầu QoS cho ba loại kiện WSN đa kiện Các kết mô cho thấy, giải pháp đề xuất làm giảm đáng kể tỷ lệ gói kiện yêu cầu độ tin cậy cao kéo dài tuổi thọ mạng WSN đa kiện Hơn nữa, trường hợp có điều kiện tải lưu lượng cao, kỹ thuật chia sẻ tải nhiều đường làm giảm độ trễ cho kiện khẩn cấp WSN đa kiện Từ khóa: Định tuyến nhận thức lượng, Định tuyến linh hoạt, Cơ chế truyền tin, Đa kiện, Mạng cảm biến không dây Received date, 02nd April, 2018 Revised manuscript, 12th May, 2018 Published, 08th June, 2018 Author affiliations: Posts and Telecommunications Institute of Technology (PTIT) *Corresponding author: hangntt@ptit.edu.vn 68 N T T Hang, N C Trinh, N T Ban, “Novel energy aware routing … sensor network.” ... requirements for multiple event type WSN In this paper, we proposes a combined solution for QoS provision, named EARPM (Energy Aware Routing Protocol for Multievent Wireless Sensor Network) for multievent. .. routing protocols, we propose our novel energy aware dynamic routing protocol for multievent WSN Our routing protocol is a renovation work from GPSR single path routing protocol [27] for event... several research papers on multipath routing protocols and energy aware routing protocols to achieve various performance benefits In ReInForM (Reliable Information Forwarding Using Multiple Paths